Leaflet is an open source JavaScript library for ineracticve maps:

library(maps)
package <U+393C><U+3E31>maps<U+393C><U+3E32> was built under R version 3.5.3
m <- leaflet() %>%
  
  setView(lng = 145.0431, lat = -37.8773, zoom = 15) %>% 
  addTiles()
            
m

NA

Note: that the use of %>% basically means use the result of the previous function as the first parameter of the next function.

The magrittr package offers a set of operators which make your code more readable by:

Is the equiv of writing:

#call the functionality of leaflet
m <- leaflet()
#set where the map is
m <- setView(m,
             lng = 145.0431, lat = -37.8773, zoom = 15) 
#Add graphics elements and layers to the map widget
m <- addTiles(m)
  
m

Customise the tile

Leaflet providers

use help(addProviderTiles) to check other available tiles.

m %>% addProviderTiles("Stamen.TonerLite")

Placing Markers on Leaflet Maps

#read some data
data <- read.csv("vet_schools_in_victoria.csv")
head(data)
#dataset is very large so only place the first 30 record
leaflet(data = data[1:30, ] ) %>%
  
  #add the map
  addTiles() %>%
  
  #add some markers
  addMarkers(~longitude, #x values
             ~latitude, #y values
             
             #label that pops up when you click
             popup= ~as.character(location))

Now plot all the data:

leaflet(data = data) %>%
  
  addTiles() %>%
  
  addMarkers(~longitude, #x values
             ~latitude, #y values
             
             #label that pops up when you click
             popup= ~as.character(location),
             
             #cluster markers when zooming out and in
             clusterOptions = markerClusterOptions())

Plotting Choropleth Maps with Leaflet

#preprocess that data
data <- read.csv("Household-heating-by-State-2008.csv", header=T) 
head(data)
#change the name of column 4 (X..Housing.Units.That.Are.Mobile.Homes) to Mobile homes
names(data)[4]<- "MobileHomes"
ag <- aggregate(MobileHomes~ States, #group by state
                FUN = mean, #find the mean number of mobile homes
                data = data) #in the data dataset
#make lower case as map data (see below) uses lower case!
ag$States <- tolower(ag$States)
head(ag)
mapStates <- map("state", 
                  fill = TRUE, 
                  plot = FALSE)
#find the related rate for each state
rates <- ag$MobileHomes[match(mapStates$names, ag$States)]
#map the data
cpal <- colorNumeric("Blues", rates) # prepare the color mapping
#create the blak cavas
leaflet(mapStates) %>%
  
  addTiles() %>%
  
  addPolygons(
    stroke = FALSE,
    smoothFactor = 0.2,
    fillOpacity = 1,
    
    #use the rate of each state to find the correct colour
    color = ~cpal(rates)
  )

Not all the states appear…

#print variables
mapStates$names
 [1] "alabama"                         "arizona"                         "arkansas"                       
 [4] "california"                      "colorado"                        "connecticut"                    
 [7] "delaware"                        "district of columbia"            "florida"                        
[10] "georgia"                         "idaho"                           "illinois"                       
[13] "indiana"                         "iowa"                            "kansas"                         
[16] "kentucky"                        "louisiana"                       "maine"                          
[19] "maryland"                        "massachusetts:martha's vineyard" "massachusetts:main"             
[22] "massachusetts:nantucket"         "michigan:north"                  "michigan:south"                 
[25] "minnesota"                       "mississippi"                     "missouri"                       
[28] "montana"                         "nebraska"                        "nevada"                         
[31] "new hampshire"                   "new jersey"                      "new mexico"                     
[34] "new york:manhattan"              "new york:main"                   "new york:staten island"         
[37] "new york:long island"            "north carolina:knotts"           "north carolina:main"            
[40] "north carolina:spit"             "north dakota"                    "ohio"                           
[43] "oklahoma"                        "oregon"                          "pennsylvania"                   
[46] "rhode island"                    "south carolina"                  "south dakota"                   
[49] "tennessee"                       "texas"                           "utah"                           
[52] "vermont"                         "virginia:chesapeake"             "virginia:chincoteague"          
[55] "virginia:main"                   "washington:san juan island"      "washington:lopez island"        
[58] "washington:orcas island"         "washington:whidbey island"       "washington:main"                
[61] "west virginia"                   "wisconsin"                       "wyoming"                        
ag$States
 [1] "#n/a"           "alabama"        "alaska"         "arizona"        "arkansas"       "california"     "colorado"      
 [8] "connecticut"    "delaware"       "florida"        "georgia"        "hawaii"         "idaho"          "illinois"      
[15] "indiana"        "iowa"           "kansas"         "kentucky"       "louisiana"      "maine"          "maryland"      
[22] "massachusetts"  "michigan"       "minnesota"      "mississippi"    "missouri"       "montana"        "nebraska"      
[29] "nevada"         "new hampshire"  "new jersey"     "new mexico"     "new york"       "north carolina" "north dakota"  
[36] "ohio"           "oklahoma"       "oregon"         "pennsylvania"   "rhode island"   "south carolina" "south dakota"  
[43] "tennessee"      "texas"          "utah"           "vermont"        "virginia"       "washington"     "west virginia" 
[50] "wisconsin"      "wyoming"       

We can see some different spelling: - e.g. “virginia:chesapeake” , “washington:main”, etc…

# split the string with : as seperator
spliteNames <- strsplit(mapStates$names, ":") 
# get first part of the origin string;
# e.g. get washington from washington:san juan island
firstPartNames <- lapply(spliteNames, function(x) x[1])  
rates <- ag$MobileHomes[match(firstPartNames, ag$States)]

Now try again:

# prepare the color mapping
cpal <- colorNumeric("Blues", rates) 
# create a blank canvas
leaflet(mapStates) %>% 
  
  # add tile
  addTiles() %>% 
  
  # draw polygons on top of the base map (tile)
  addPolygons( 
    stroke = FALSE, 
    smoothFactor = 0.2, 
    fillOpacity = 1,
    color = ~cpal(rates) # use the rate of each state to find the correct color
  )

Shapefiles

#install.packages(rdgal)
library(rgdal)
package <U+393C><U+3E31>rgdal<U+393C><U+3E32> was built under R version 3.5.3Loading required package: sp
package <U+393C><U+3E31>sp<U+393C><U+3E32> was built under R version 3.5.3rgdal: version: 1.4-3, (SVN revision 828)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
 Path to GDAL shared files: C:/Users/pasulj/Documents/R/win-library/3.5/rgdal/gdal
 GDAL binary built with GEOS: TRUE 
 Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
 Path to PROJ.4 shared files: C:/Users/pasulj/Documents/R/win-library/3.5/rgdal/proj
 Linking to sp version: 1.3-1 
#read data
world_map <- readOGR("ne_50m_admin_0_countries.shp")
OGR data source with driver: ESRI Shapefile 
Source: "C:\Users\pasulj\OneDrive - Bond University\GDDS\FIT5147\Week 3\Activities\ne_50m_admin_0_countries.shp", layer: "ne_50m_admin_0_countries"
with 241 features
It has 63 fields
head(world_map)
An object of class "SpatialPolygonsDataFrame"
Slot "data":

Slot "polygons":
[[1]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -69.98268  12.52088

Slot "area":
[1] 0.01286311

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -69.89912 12.45200
 [2,] -69.89570 12.42300
 [3,] -69.94219 12.43853
 [4,] -70.00415 12.50049
 [5,] -70.06611 12.54697
 [6,] -70.05088 12.59707
 [7,] -70.03511 12.61411
 [8,] -69.97314 12.56763
 [9,] -69.91182 12.48047
[10,] -69.89912 12.45200



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -69.98268  12.52088

Slot "ID":
[1] "0"

Slot "area":
[1] 0.01286311


[[2]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 66.00473 33.83523

Slot "area":
[1] 62.55793

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
  [1,] 74.89131 37.23164
  [2,] 74.84023 37.22505
  [3,] 74.76738 37.24917
  [4,] 74.73896 37.28564
  [5,] 74.72666 37.29072
  [6,] 74.66895 37.26670
  [7,] 74.55898 37.23662
  [8,] 74.37217 37.15771
  [9,] 74.37617 37.13735
 [10,] 74.49795 37.05723
 [11,] 74.52646 37.03066
 [12,] 74.54141 37.02217
 [13,] 74.43105 36.98369
 [14,] 74.19473 36.89687
 [15,] 74.03887 36.82573
 [16,] 74.00186 36.82310
 [17,] 73.90781 36.85293
 [18,] 73.76914 36.88848
 [19,] 73.73184 36.88779
 [20,] 73.41113 36.88169
 [21,] 73.11680 36.86855
 [22,] 72.99375 36.85161
 [23,] 72.76621 36.83501
 [24,] 72.62285 36.82959
 [25,] 72.53135 36.80200
 [26,] 72.43115 36.76582
 [27,] 72.32695 36.74238
 [28,] 72.24980 36.73472
 [29,] 72.15674 36.70088
 [30,] 72.09561 36.63374
 [31,] 71.92070 36.53418
 [32,] 71.82227 36.48608
 [33,] 71.77266 36.43184
 [34,] 71.71641 36.42656
 [35,] 71.62051 36.43647
 [36,] 71.54590 36.37769
 [37,] 71.46328 36.29326
 [38,] 71.31260 36.17119
 [39,] 71.23291 36.12178
 [40,] 71.18506 36.04209
 [41,] 71.22021 36.00068
 [42,] 71.34287 35.93853
 [43,] 71.39756 35.88018
 [44,] 71.42754 35.83374
 [45,] 71.48359 35.71460
 [46,] 71.51904 35.59751
 [47,] 71.57197 35.54683
 [48,] 71.58740 35.46084
 [49,] 71.60059 35.40791
 [50,] 71.57197 35.37041
 [51,] 71.54551 35.32852
 [52,] 71.54551 35.28887
 [53,] 71.57725 35.24800
 [54,] 71.60527 35.21177
 [55,] 71.62051 35.18301
 [56,] 71.60166 35.15068
 [57,] 71.54551 35.10142
 [58,] 71.51709 35.05112
 [59,] 71.45508 34.96694
 [60,] 71.35811 34.90962
 [61,] 71.29414 34.86772
 [62,] 71.22578 34.77954
 [63,] 71.11328 34.68159
 [64,] 71.06563 34.59961
 [65,] 71.01631 34.55464
 [66,] 70.96563 34.53037
 [67,] 70.97891 34.48628
 [68,] 71.02295 34.43115
 [69,] 71.09570 34.36943
 [70,] 71.09238 34.27324
 [71,] 71.08906 34.20405
 [72,] 71.09131 34.12026
 [73,] 71.05156 34.04971
 [74,] 70.84844 33.98188
 [75,] 70.65400 33.95229
 [76,] 70.41572 33.95044
 [77,] 70.32568 33.96113
 [78,] 70.25361 33.97598
 [79,] 69.99473 34.05181
 [80,] 69.88965 34.00728
 [81,] 69.86807 33.89766
 [82,] 70.05664 33.71987
 [83,] 70.13418 33.62075
 [84,] 70.21973 33.45469
 [85,] 70.28418 33.36904
 [86,] 70.26113 33.28901
 [87,] 70.09023 33.19810
 [88,] 69.92012 33.11250
 [89,] 69.70371 33.09473
 [90,] 69.56777 33.06416
 [91,] 69.50156 33.02007
 [92,] 69.45312 32.83281
 [93,] 69.40459 32.76426
 [94,] 69.40537 32.68271
 [95,] 69.35947 32.59033
 [96,] 69.28994 32.53057
 [97,] 69.24141 32.43354
 [98,] 69.25654 32.24946
 [99,] 69.27930 31.93682
[100,] 69.18691 31.83809
[101,] 69.08311 31.73848
[102,] 68.97344 31.66738
[103,] 68.86895 31.63423
[104,] 68.78232 31.64644
[105,] 68.71367 31.70806
[106,] 68.67324 31.75972
[107,] 68.59766 31.80298
[108,] 68.52070 31.79414
[109,] 68.44326 31.75449
[110,] 68.31982 31.76768
[111,] 68.21396 31.80737
[112,] 68.16104 31.80298
[113,] 68.13018 31.76328
[114,] 68.01719 31.67798
[115,] 67.73984 31.54819
[116,] 67.62676 31.53877
[117,] 67.57822 31.50649
[118,] 67.59756 31.45332
[119,] 67.64707 31.40996
[120,] 67.73350 31.37925
[121,] 67.73789 31.34395
[122,] 67.66152 31.31299
[123,] 67.59639 31.27769
[124,] 67.45283 31.23462
[125,] 67.28730 31.21782
[126,] 67.11592 31.24292
[127,] 67.02773 31.30024
[128,] 66.92432 31.30562
[129,] 66.82930 31.26367
[130,] 66.73135 31.19453
[131,] 66.62422 31.04604
[132,] 66.59580 31.01997
[133,] 66.56680 30.99658
[134,] 66.49736 30.96455
[135,] 66.39717 30.91221
[136,] 66.34688 30.80278
[137,] 66.28691 30.60791
[138,] 66.30098 30.50298
[139,] 66.30547 30.32114
[140,] 66.28184 30.19346
[141,] 66.23848 30.10962
[142,] 66.24717 30.04351
[143,] 66.31338 29.96855
[144,] 66.28691 29.92002
[145,] 66.23125 29.86572
[146,] 66.17705 29.83560
[147,] 65.96162 29.77891
[148,] 65.66621 29.70132
[149,] 65.47100 29.65156
[150,] 65.18047 29.57764
[151,] 65.09551 29.55947
[152,] 64.91895 29.55278
[153,] 64.82734 29.56416
[154,] 64.70352 29.56714
[155,] 64.52109 29.56450
[156,] 64.39375 29.54434
[157,] 64.26611 29.50693
[158,] 64.17217 29.46035
[159,] 64.11797 29.41426
[160,] 64.09873 29.39194
[161,] 63.97100 29.43008
[162,] 63.56758 29.49800
[163,] 62.47656 29.40835
[164,] 62.37344 29.42539
[165,] 62.00098 29.53042
[166,] 61.52148 29.66567
[167,] 61.22441 29.74941
[168,] 60.84336 29.85869
[169,] 61.10410 30.12842
[170,] 61.33164 30.36372
[171,] 61.55947 30.59937
[172,] 61.78418 30.83193
[173,] 61.81084 30.91328
[174,] 61.81426 31.07256
[175,] 61.75508 31.28530
[176,] 61.66016 31.38242
[177,] 61.34648 31.42163
[178,] 61.11074 31.45112
[179,] 60.85410 31.48325
[180,] 60.82070 31.49517
[181,] 60.79160 31.66060
[182,] 60.80430 31.73447
[183,] 60.78750 31.87720
[184,] 60.78994 31.98711
[185,] 60.82725 32.16797
[186,] 60.82930 32.24941
[187,] 60.71045 32.60000
[188,] 60.64453 32.79438
[189,] 60.57656 32.99487
[190,] 60.56191 33.05879
[191,] 60.56055 33.13784
[192,] 60.71807 33.32354
[193,] 60.76689 33.36382
[194,] 60.85928 33.45625
[195,] 60.91699 33.50522
[196,] 60.90693 33.53896
[197,] 60.80645 33.55869
[198,] 60.65459 33.56040
[199,] 60.57383 33.58833
[200,] 60.51084 33.63892
[201,] 60.48594 33.71191
[202,] 60.52705 33.84199
[203,] 60.48574 34.09478
[204,] 60.57021 34.21963
[205,] 60.64268 34.30718
[206,] 60.88945 34.31943
[207,] 60.80391 34.41802
[208,] 60.76260 34.47524
[209,] 60.73613 34.49180
[210,] 60.72627 34.51826
[211,] 60.73945 34.54473
[212,] 60.80234 34.55464
[213,] 60.84531 34.58770
[214,] 60.91475 34.63398
[215,] 60.95117 34.65386
[216,] 60.95781 34.71006
[217,] 60.99082 34.74976
[218,] 61.04043 34.79937
[219,] 61.08008 34.85562
[220,] 61.07021 34.92173
[221,] 61.10664 35.00112
[222,] 61.12314 35.05073
[223,] 61.14961 35.09375
[224,] 61.12646 35.15654
[225,] 61.10664 35.20947
[226,] 61.10000 35.27231
[227,] 61.13965 35.28887
[228,] 61.18926 35.31201
[229,] 61.19922 35.36162
[230,] 61.22568 35.42446
[231,] 61.24551 35.47407
[232,] 61.27852 35.51377
[233,] 61.28184 35.55342
[234,] 61.26201 35.61958
[235,] 61.34473 35.62949
[236,] 61.37773 35.59312
[237,] 61.42178 35.54580
[238,] 61.54277 35.45786
[239,] 61.62100 35.43232
[240,] 61.71973 35.41943
[241,] 61.84102 35.43149
[242,] 61.93809 35.44790
[243,] 61.98389 35.44370
[244,] 62.08965 35.37969
[245,] 62.21309 35.28994
[246,] 62.25283 35.25024
[247,] 62.27119 35.18911
[248,] 62.30781 35.17080
[249,] 62.38662 35.23125
[250,] 62.46289 35.25137
[251,] 62.53311 35.23989
[252,] 62.61055 35.23315
[253,] 62.68809 35.25532
[254,] 62.72266 35.27134
[255,] 62.85801 35.34966
[256,] 62.98027 35.40918
[257,] 63.05664 35.44580
[258,] 63.08418 35.56807
[259,] 63.11934 35.63755
[260,] 63.16973 35.67812
[261,] 63.15078 35.72827
[262,] 63.12998 35.76675
[263,] 63.10859 35.81870
[264,] 63.12998 35.84619
[265,] 63.17891 35.85845
[266,] 63.30166 35.85840
[267,] 63.51699 35.91313
[268,] 63.69658 35.96782
[269,] 63.86250 36.01235
[270,] 63.93809 36.01973
[271,] 64.00967 36.01211
[272,] 64.04238 36.02510
[273,] 64.05137 36.06763
[274,] 64.09219 36.11270
[275,] 64.18438 36.14893
[276,] 64.35801 36.22607
[277,] 64.51104 36.34067
[278,] 64.56582 36.42759
[279,] 64.60254 36.55454
[280,] 64.67432 36.75020
[281,] 64.75313 36.96479
[282,] 64.78242 37.05928
[283,] 64.81631 37.13208
[284,] 64.95156 37.19355
[285,] 65.08965 37.23794
[286,] 65.30361 37.24678
[287,] 65.55498 37.25117
[288,] 65.60801 37.36841
[289,] 65.64121 37.46782
[290,] 65.68301 37.51914
[291,] 65.74385 37.56084
[292,] 65.76504 37.56914
[293,] 65.90068 37.50811
[294,] 66.10840 37.41475
[295,] 66.35029 37.36816
[296,] 66.47188 37.34473
[297,] 66.52227 37.34849
[298,] 66.82773 37.37129
[299,] 67.06885 37.33481
[300,] 67.19551 37.23521
[301,] 67.31973 37.20957
[302,] 67.44170 37.25801
[303,] 67.51729 37.26665
[304,] 67.54648 37.23564
[305,] 67.60742 37.22251
[306,] 67.70000 37.22725
[307,] 67.75293 37.19980
[308,] 67.75898 37.17222
[309,] 67.76602 37.14014
[310,] 67.83447 37.06421
[311,] 67.95801 36.97202
[312,] 68.06777 36.94980
[313,] 68.21211 37.02153
[314,] 68.26094 37.01309
[315,] 68.28477 37.03633
[316,] 68.29951 37.08843
[317,] 68.38691 37.13750
[318,] 68.54648 37.18345
[319,] 68.63701 37.22446
[320,] 68.66914 37.25840
[321,] 68.72324 37.26802
[322,] 68.78203 37.25801
[323,] 68.82373 37.27070
[324,] 68.83848 37.30283
[325,] 68.85537 37.31685
[326,] 68.88525 37.32808
[327,] 68.91182 37.33394
[328,] 68.96045 37.32505
[329,] 69.05000 37.26650
[330,] 69.18018 37.15830
[331,] 69.26484 37.10840
[332,] 69.30391 37.11694
[333,] 69.35381 37.15005
[334,] 69.41445 37.20776
[335,] 69.42969 37.29087
[336,] 69.39922 37.39932
[337,] 69.42012 37.48672
[338,] 69.49209 37.55308
[339,] 69.62578 37.59404
[340,] 69.82090 37.60957
[341,] 69.94063 37.60029
[342,] 69.98496 37.56616
[343,] 70.04473 37.54722
[344,] 70.11982 37.54351
[345,] 70.18867 37.58247
[346,] 70.25146 37.66416
[347,] 70.25498 37.76538
[348,] 70.19941 37.88604
[349,] 70.21465 37.92441
[350,] 70.23877 37.94121
[351,] 70.31328 37.98481
[352,] 70.41777 38.07544
[353,] 70.51855 38.19199
[354,] 70.61582 38.33442
[355,] 70.73594 38.42256
[356,] 70.87891 38.45640
[357,] 71.05215 38.41787
[358,] 71.25586 38.30698
[359,] 71.33271 38.17026
[360,] 71.28281 38.00791
[361,] 71.27852 37.91841
[362,] 71.31992 37.90186
[363,] 71.38965 37.90630
[364,] 71.48779 37.93188
[365,] 71.55195 37.93315
[366,] 71.58223 37.91011
[367,] 71.58037 37.86426
[368,] 71.54619 37.79565
[369,] 71.50508 37.60293
[370,] 71.47969 37.43604
[371,] 71.45479 37.27183
[372,] 71.43291 37.12754
[373,] 71.47188 37.01509
[374,] 71.53086 36.84512
[375,] 71.59746 36.73291
[376,] 71.66563 36.69692
[377,] 71.73379 36.68403
[378,] 71.80205 36.69429
[379,] 71.94199 36.76646
[380,] 72.15352 36.90054
[381,] 72.35879 36.98291
[382,] 72.65742 37.02905
[383,] 72.75703 37.17271
[384,] 72.89551 37.26753
[385,] 73.21113 37.40850
[386,] 73.38291 37.46226
[387,] 73.48135 37.47168
[388,] 73.60469 37.44604
[389,] 73.63262 37.43721
[390,] 73.65713 37.43047
[391,] 73.72061 37.41875
[392,] 73.73379 37.37578
[393,] 73.71729 37.32944
[394,] 73.64883 37.29121
[395,] 73.62754 37.26157
[396,] 73.65352 37.23936
[397,] 73.74961 37.23179
[398,] 73.94883 37.28315
[399,] 74.07773 37.31621
[400,] 74.16709 37.32944
[401,] 74.20352 37.37246
[402,] 74.25967 37.41543
[403,] 74.34902 37.41875
[404,] 74.44492 37.39561
[405,] 74.52422 37.38237
[406,] 74.65938 37.39448
[407,] 74.73057 37.35703
[408,] 74.83047 37.28594
[409,] 74.87539 37.24199
[410,] 74.89131 37.23164



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] 66.00473 33.83523

Slot "ID":
[1] "1"

Slot "area":
[1] 62.55793


[[3]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1]  17.56444 -12.33195

Slot "area":
[1] 103.0391

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]       [,2]
  [1,] 14.19082  -5.875977
  [2,] 14.39863  -5.892676
  [3,] 14.65791  -5.888867
  [4,] 14.74941  -5.880078
  [5,] 15.08936  -5.874512
  [6,] 15.42500  -5.868848
  [7,] 15.72695  -5.863867
  [8,] 16.06016  -5.864941
  [9,] 16.31523  -5.865625
 [10,] 16.43145  -5.900195
 [11,] 16.53711  -5.965820
 [12,] 16.58516  -6.025293
 [13,] 16.60801  -6.051562
 [14,] 16.63955  -6.114551
 [15,] 16.69727  -6.164258
 [16,] 16.71777  -6.241406
 [17,] 16.70098  -6.345996
 [18,] 16.70938  -6.471680
 [19,] 16.74297  -6.618457
 [20,] 16.81309  -6.772559
 [21,] 16.91943  -6.933984
 [22,] 16.96582  -7.062109
 [23,] 16.95205  -7.157031
 [24,] 16.98477  -7.257422
 [25,] 17.06377  -7.363086
 [26,] 17.12158  -7.419043
 [27,] 17.15508  -7.461328
 [28,] 17.24502  -7.623340
 [29,] 17.41133  -7.881934
 [30,] 17.53604  -8.075879
 [31,] 17.57959  -8.099023
 [32,] 17.64336  -8.090723
 [33,] 17.77881  -8.071387
 [34,] 17.91309  -8.067676
 [35,] 18.00879  -8.107617
 [36,] 18.04717  -8.100781
 [37,] 18.19150  -8.023828
 [38,] 18.33486  -8.000293
 [39,] 18.48467  -7.968555
 [40,] 18.56270  -7.935938
 [41,] 18.65342  -7.936035
 [42,] 18.89834  -7.998145
 [43,] 18.94443  -8.001465
 [44,] 19.14268  -8.001465
 [45,] 19.34082  -7.966602
 [46,] 19.36992  -7.706543
 [47,] 19.37168  -7.655078
 [48,] 19.41934  -7.557324
 [49,] 19.47988  -7.472168
 [50,] 19.48740  -7.390723
 [51,] 19.48379  -7.279492
 [52,] 19.52764  -7.144434
 [53,] 19.66035  -7.037109
 [54,] 19.87520  -6.986328
 [55,] 19.99746  -6.976465
 [56,] 20.19004  -6.946289
 [57,] 20.48223  -6.915820
 [58,] 20.59004  -6.919922
 [59,] 20.59873  -6.935156
 [60,] 20.53691  -7.121777
 [61,] 20.53584  -7.182812
 [62,] 20.55840  -7.244434
 [63,] 20.60781  -7.277734
 [64,] 20.91094  -7.281445
 [65,] 21.19033  -7.284961
 [66,] 21.51084  -7.296680
 [67,] 21.75107  -7.305469
 [68,] 21.78164  -7.314648
 [69,] 21.80605  -7.328613
 [70,] 21.84160  -7.420996
 [71,] 21.83359  -7.601660
 [72,] 21.78008  -7.865430
 [73,] 21.80088  -8.111914
 [74,] 21.89590  -8.341113
 [75,] 21.90537  -8.693359
 [76,] 21.87188  -8.903516
 [77,] 21.82949  -9.168457
 [78,] 21.81318  -9.468750
 [79,] 21.85664  -9.594238
 [80,] 21.94863  -9.725586
 [81,] 22.08916  -9.862793
 [82,] 22.19775 -10.040625
 [83,] 22.27451 -10.259082
 [84,] 22.30244 -10.396680
 [85,] 22.28164 -10.453320
 [86,] 22.28320 -10.551562
 [87,] 22.30703 -10.691309
 [88,] 22.28047 -10.783984
 [89,] 22.20352 -10.829492
 [90,] 22.17793 -10.892285
 [91,] 22.21670 -11.012695
 [92,] 22.22617 -11.121973
 [93,] 22.25664 -11.163672
 [94,] 22.27881 -11.194141
 [95,] 22.31494 -11.198633
 [96,] 22.39297 -11.159473
 [97,] 22.48613 -11.086719
 [98,] 22.56104 -11.055859
 [99,] 22.66650 -11.059766
[100,] 22.81475 -11.080273
[101,] 23.07627 -11.087891
[102,] 23.15674 -11.074805
[103,] 23.40020 -10.976465
[104,] 23.46396 -10.969336
[105,] 23.55996 -10.978613
[106,] 23.69639 -11.007617
[107,] 23.83389 -11.013672
[108,] 23.90117 -10.983203
[109,] 23.90732 -10.943457
[110,] 23.92871 -10.891504
[111,] 23.96650 -10.871777
[112,] 23.98828 -11.002832
[113,] 24.01006 -11.184766
[114,] 24.02559 -11.315625
[115,] 24.04141 -11.374121
[116,] 24.04668 -11.405371
[117,] 24.02930 -11.439160
[118,] 24.01465 -11.517676
[119,] 23.98682 -11.587207
[120,] 23.97100 -11.635840
[121,] 23.98389 -11.725000
[122,] 23.97344 -11.852930
[123,] 23.96230 -11.987891
[124,] 23.95889 -12.117773
[125,] 23.99648 -12.350684
[126,] 23.99131 -12.422168
[127,] 23.94473 -12.543750
[128,] 23.90938 -12.636133
[129,] 23.88652 -12.743262
[130,] 23.88242 -12.799023
[131,] 23.96807 -12.956934
[132,] 23.96299 -12.988477
[133,] 23.89746 -12.998242
[134,] 23.84316 -13.000977
[135,] 23.63584 -13.000977
[136,] 23.33867 -13.000977
[137,] 23.04150 -13.000977
[138,] 22.74434 -13.000977
[139,] 22.47100 -13.000977
[140,] 22.20957 -13.000977
[141,] 21.97891 -13.000977
[142,] 21.97900 -13.156836
[143,] 21.97910 -13.477734
[144,] 21.97910 -13.798730
[145,] 21.97930 -14.119629
[146,] 21.97939 -14.440527
[147,] 21.97949 -14.761426
[148,] 21.97959 -15.082324
[149,] 21.97969 -15.403223
[150,] 21.97979 -15.724121
[151,] 21.97979 -15.955566
[152,] 22.04023 -16.262793
[153,] 22.15068 -16.597168
[154,] 22.19395 -16.628125
[155,] 22.30508 -16.689551
[156,] 22.45947 -16.815137
[157,] 22.54600 -16.910254
[158,] 22.72197 -17.075293
[159,] 22.95586 -17.285742
[160,] 23.18164 -17.474414
[161,] 23.38066 -17.640625
[162,] 23.06826 -17.698828
[163,] 22.62402 -17.781641
[164,] 22.32422 -17.837500
[165,] 21.96084 -17.905176
[166,] 21.71846 -17.947754
[167,] 21.41689 -18.000684
[168,] 21.36875 -17.999512
[169,] 21.28789 -17.962988
[170,] 21.11348 -17.955762
[171,] 20.90830 -18.006055
[172,] 20.74551 -18.019727
[173,] 20.62510 -17.996680
[174,] 20.50762 -17.952539
[175,] 20.39297 -17.887402
[176,] 20.19434 -17.863672
[177,] 19.91182 -17.881348
[178,] 19.63936 -17.868652
[179,] 19.37715 -17.825488
[180,] 19.18945 -17.808496
[181,] 19.07646 -17.817676
[182,] 18.95527 -17.803516
[183,] 18.82598 -17.766309
[184,] 18.71807 -17.703223
[185,] 18.58818 -17.570020
[186,] 18.48662 -17.442773
[187,] 18.46035 -17.424609
[188,] 18.42822 -17.405176
[189,] 18.39639 -17.399414
[190,] 18.10879 -17.395996
[191,] 17.83535 -17.392773
[192,] 17.67881 -17.392578
[193,] 17.29629 -17.391992
[194,] 16.91367 -17.391406
[195,] 16.53105 -17.390820
[196,] 16.14844 -17.390234
[197,] 15.76582 -17.389648
[198,] 15.38320 -17.389160
[199,] 15.00059 -17.388574
[200,] 14.61797 -17.387988
[201,] 14.41475 -17.387695
[202,] 14.22588 -17.397754
[203,] 14.01748 -17.408887
[204,] 13.98740 -17.404199
[205,] 13.93799 -17.388770
[206,] 13.90420 -17.360742
[207,] 13.79199 -17.288379
[208,] 13.69434 -17.233496
[209,] 13.56172 -17.141211
[210,] 13.47598 -17.040039
[211,] 13.40371 -17.007812
[212,] 13.27568 -16.989551
[213,] 13.17949 -16.971680
[214,] 13.10117 -16.967676
[215,] 12.96318 -17.015430
[216,] 12.85928 -17.062598
[217,] 12.78516 -17.108203
[218,] 12.65654 -17.160547
[219,] 12.54814 -17.212695
[220,] 12.35928 -17.205859
[221,] 12.31846 -17.213379
[222,] 12.21338 -17.209961
[223,] 12.11436 -17.164551
[224,] 12.01396 -17.168555
[225,] 11.90254 -17.226562
[226,] 11.74307 -17.249219
[227,] 11.78008 -16.871289
[228,] 11.81895 -16.704102
[229,] 11.81992 -16.504297
[230,] 11.79697 -15.986426
[231,] 11.76943 -15.915332
[232,] 11.75088 -15.831934
[233,] 11.84971 -15.768359
[234,] 11.89990 -15.719824
[235,] 11.96787 -15.633984
[236,] 12.01611 -15.513672
[237,] 12.07324 -15.248242
[238,] 12.28047 -14.637500
[239,] 12.37891 -14.039063
[240,] 12.50371 -13.755469
[241,] 12.55049 -13.437793
[242,] 12.89766 -13.027734
[243,] 12.98320 -12.775684
[244,] 13.16270 -12.652148
[245,] 13.41699 -12.520410
[246,] 13.59795 -12.286133
[247,] 13.68555 -12.123828
[248,] 13.78535 -11.812793
[249,] 13.78428 -11.487988
[250,] 13.84746 -11.054395
[251,] 13.83359 -10.929688
[252,] 13.73896 -10.757129
[253,] 13.72139 -10.633594
[254,] 13.63350 -10.512305
[255,] 13.53945 -10.420703
[256,] 13.49541 -10.257129
[257,] 13.33223  -9.998926
[258,] 13.28750  -9.826758
[259,] 13.20938  -9.703223
[260,] 13.19688  -9.550684
[261,] 13.15566  -9.389648
[262,] 13.07598  -9.230371
[263,] 12.99854  -9.048047
[264,] 12.99854  -8.991016
[265,] 13.04678  -8.922266
[266,] 13.09277  -8.899707
[267,] 13.07725  -8.934277
[268,] 13.04658  -8.975195
[269,] 13.05381  -9.006836
[270,] 13.35898  -8.687207
[271,] 13.37832  -8.624707
[272,] 13.36807  -8.554785
[273,] 13.36641  -8.469238
[274,] 13.37852  -8.369727
[275,] 13.09082  -7.780176
[276,] 12.86230  -7.231836
[277,] 12.82344  -6.954785
[278,] 12.52129  -6.590332
[279,] 12.40215  -6.353418
[280,] 12.33428  -6.187305
[281,] 12.28330  -6.124316
[282,] 12.30254  -6.092578
[283,] 12.38037  -6.084277
[284,] 12.55352  -6.045898
[285,] 12.79063  -6.003906
[286,] 13.00977  -5.907617
[287,] 13.06816  -5.864844
[288,] 13.18438  -5.856250
[289,] 13.30264  -5.881836
[290,] 13.34648  -5.863379
[291,] 13.37148  -5.861816
[292,] 13.64902  -5.861719
[293,] 13.76455  -5.855176
[294,] 13.97852  -5.857227
[295,] 14.11377  -5.865137
[296,] 14.19082  -5.875977


[[2]]
An object of class "Polygon"
Slot "labpt":
[1] 12.448313 -5.039424

Slot "area":
[1] 0.5481372

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]      [,2]
 [1,] 12.25527 -5.746484
 [2,] 12.21367 -5.758691
 [3,] 12.19902 -5.731934
 [4,] 12.15547 -5.632715
 [5,] 12.18008 -5.538672
 [6,] 12.20654 -5.468262
 [7,] 12.17715 -5.324805
 [8,] 12.11055 -5.197168
 [9,] 12.03994 -5.035156
[10,] 12.01836 -5.004297
[11,] 12.07754 -4.952148
[12,] 12.16709 -4.837695
[13,] 12.20430 -4.778613
[14,] 12.30791 -4.765527
[15,] 12.34668 -4.724121
[16,] 12.37402 -4.657715
[17,] 12.38457 -4.619141
[18,] 12.50146 -4.587500
[19,] 12.64170 -4.531152
[20,] 12.71943 -4.469727
[21,] 12.79824 -4.430566
[22,] 12.84814 -4.428906
[23,] 12.88105 -4.445117
[24,] 12.97139 -4.551758
[25,] 13.04805 -4.619238
[26,] 13.07275 -4.634766
[27,] 13.05732 -4.651074
[28,] 12.94746 -4.695312
[29,] 12.82969 -4.736621
[30,] 12.67480 -4.905371
[31,] 12.59619 -4.978418
[32,] 12.57354 -4.996582
[33,] 12.50273 -5.036914
[34,] 12.45146 -5.071484
[35,]
 12.45322 -5.090625
[36,] 12.48740 -5.112695
[37,] 12.52236 -5.148926
[38,] 12.51895 -5.424609
[39,] 12.50371 -5.695801
[40,] 12.48457 -5.718750
[41,] 12.38604 -5.727734
[42,] 12.25527 -5.746484



Slot "plotOrder":
[1] 1 2

Slot "labpt":
[1]  17.56444 -12.33195

Slot "ID":
[1] "2"

Slot "area":
[1] 103.5873


[[4]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -63.06499  18.22396

Slot "area":
[1] 0.007091551

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]     [,2]
[1,] -63.00122 18.22178
[2,] -63.16001 18.17139
[3,] -63.15332 18.20029
[4,] -63.02603 18.26973
[5,] -62.97959 18.26479
[6,] -63.00122 18.22178



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -63.06499  18.22396

Slot "ID":
[1] "3"

Slot "area":
[1] 0.007091551


[[5]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 20.04983 41.14245

Slot "area":
[1] 3.043388

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
  [1,] 20.06396 42.54727
  [2,] 20.10352 42.52466
  [3,] 20.18574 42.42588
  [4,] 20.24053 42.33896
  [5,] 20.34824 42.30879
  [6,] 20.40830 42.27495
  [7,] 20.48545 42.22339
  [8,] 20.52285 42.17148
  [9,] 20.57539 42.01309
 [10,] 20.58145 41.91743
 [11,] 20.56621 41.87368
 [12,] 20.55313 41.86235
 [13,] 20.50518 41.70649
 [14,] 20.51660 41.62705
 [15,] 20.51621 41.57476
 [16,] 20.47559 41.55410
 [17,] 20.44863 41.52129
 [18,] 20.49238 41.39141
 [19,] 20.48701 41.33608
 [20,] 20.48896 41.27261
 [21,] 20.56787 41.12783
 [22,] 20.61445 41.08306
 [23,] 20.65605 41.06167
 [24,] 20.70928 40.92837
 [25,] 20.74082 40.90527
 [26,] 20.87021 40.91792
 [27,] 20.93350 40.90312
 [28,] 20.95859 40.87153
 [29,] 20.96426 40.84990
 [30,] 20.95576 40.77529
 [31,] 20.98789 40.71777
 [32,] 21.03105 40.65864
 [33,] 21.03086 40.62246
 [34,] 21.00195 40.56338
 [35,] 20.95020 40.49438
 [36,] 20.88164 40.46792
 [37,] 20.80605 40.44546
 [38,] 20.77002 40.39189
 [39,] 20.75166 40.33491
 [40,] 20.71787 40.29268
 [41,] 20.69697 40.24639
 [42,] 20.66494 40.15176
 [43,] 20.65742 40.11738
 [44,] 20.60625 40.08267
 [45,] 20.52705 40.06851
 [46,] 20.45605 40.06558
 [47,] 20.40801 40.04946
 [48,] 20.38369 40.01719
 [49,] 20.33848 39.99106
 [50,] 20.31113 39.97944
 [51,] 20.31133 39.95078
 [52,] 20.34424 39.89062
 [53,] 20.38164 39.84180
 [54,] 20.38242 39.80264
 [55,] 20.36406 39.79175
 [56,] 20.30615 39.79668
 [57,] 20.29385 39.78223
 [58,] 20.28760 39.73857
 [59,] 20.27207 39.70117
 [60,] 20.24824 39.67837
 [61,] 20.20684 39.65352
 [62,] 20.13105 39.66162
 [63,] 20.05977 39.69912
 [64,] 20.02256 39.71069
 [65,] 20.00127 39.70942
 [66,] 19.99561 39.80103
 [67,] 19.96484 39.87227
 [68,] 19.85186 40.04355
 [69,] 19.48457 40.20996
 [70,] 19.39814 40.28486
 [71,] 19.36016 40.34771
 [72,] 19.32227 40.40708
 [73,] 19.35859 40.40874
 [74,] 19.39453 40.39370
 [75,] 19.44053 40.37568
 [76,] 19.45918 40.40537
 [77,] 19.43926 40.47026
 [78,] 19.34463 40.62207
 [79,] 19.33750 40.66382
 [80,] 19.38389 40.79072
 [81,] 19.46123 40.93330
 [82,] 19.45605 41.10605
 [83,] 19.48008 41.23638
 [84,] 19.45342 41.32100
 [85,] 19.44063 41.42476
 [86,] 19.49736 41.56270
 [87,] 19.54580 41.59683
 [88,] 19.57568 41.64043
 [89,] 19.57754 41.78750
 [90,] 19.46826 41.85615
 [91,] 19.34238 41.86909
 [92,] 19.34551 41.91885
 [93,] 19.36113 41.99775
 [94,] 19.35215 42.02402
 [95,] 19.36143 42.06909
 [96,] 19.33086 42.12930
 [97,] 19.28066 42.17256
 [98,] 19.32900 42.24927
 [99,] 19.39961 42.34189
[100,] 19.46514 42.41538
[101,] 19.54453 42.49194
[102,] 19.59746 42.56543
[103,] 19.65449 42.62856
[104,] 19.70342 42.64795
[105,] 19.72783 42.63452
[106,] 19.74072 42.60693
[107,] 19.73779 42.52515
[108,] 19.75449 42.49692
[109,] 19.78828 42.47617
[110,] 19.85977 42.48633
[111,] 19.93906 42.50669
[112,] 20.04570 42.54990
[113,] 20.06396 42.54727



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] 20.04983 41.14245

Slot "ID":
[1] "4"

Slot "area":
[1] 3.043388


[[6]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 20.51543 60.04434

Slot "area":
[1] 0.008765941

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]     [,2]
 [1,] 20.61133 60.04067
 [2,] 20.60342 60.01694
 [3,] 20.52178 60.01167
 [4,] 20.48750 60.03276
 [5,] 20.41123 60.03013
 [6,] 20.39795 60.04067
 [7,] 20.42959 60.06172
 [8,] 20.49014 60.07490
 [9,] 20.56914 60.06963
[10,] 20.61133 60.04067


[[2]]
An object of class "Polygon"
Slot "labpt":
[1] 19.58766 60.19482

Slot "area":
[1] 0.01083552

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]     [,2]
 [1,] 19.66230 60.18716
 [2,] 19.66748 60.16475
 [3,] 19.62920 60.17036
 [4,] 19.59980 60.16270
 [5,] 19.57988 60.13506
 [6,] 19.53652 60.14497
 [7,] 19.51904 60.18457
 [8,] 19.55137 60.24385
 [9,] 19.62881 60.24609
[10,] 19.66230 60.18716


[[3]]
An object of class "Polygon"
Slot "labpt":
[1] 19.94401 60.23133

Slot "area":
[1] 0.1041173

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
          [,1]     [,2]
 [1,] 19.98955 60.35117
 [2,] 20.02021 60.35088
 [3,] 20.03389 60.35933
 [4,] 20.08740 60.35342
 [5,] 20.16787 60.31470
 [6,] 20.18408 60.29375
 [7,] 20.23955 60.28301
 [8,] 20.25889 60.26128
 [9,] 20.19473 60.19355
[10,] 20.15508 60.19229
[11,] 20.12549 60.20088
[12,] 20.07324 60.19346
[13,] 20.04258 60.18066
[14,] 20.03232 60.15249
[15,] 20.03398 60.09355
[16,] 19.79980 60.08174
[17,] 19.74600 60.09897
[18,] 19.67227 60.23301
[19,] 19.68691 60.26763
[20,] 19.73652 60.28237
[21,] 19.77900 60.28555
[22,] 19.78525 60.21338
[23,] 19.84766 60.22056
[24,] 19.86719 60.26812
[25,] 19.87158 60.30161
[26,] 19.85469 60.31851
[27,] 19.81230 60.33159
[28,] 19.78779 60.35405
[29,] 19.82305 60.39019
[30,] 19.88828 60.40581
[31,] 19.94453 60.35752
[32,] 19.98955 60.35117



Slot "plotOrder":
[1] 3 2 1

Slot "labpt":
[1] 19.94401 60.23133

Slot "ID":
[1] "5"

Slot "area":
[1] 0.1237188



Slot "plotOrder":
[1] 3 2 5 6 1 4

Slot "bbox":
        min      max
x -70.06611 74.89131
y -18.01973 60.40581

Slot "proj4string":
CRS arguments:
 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
gdp_data <- read.csv("WorldGDP.csv")
head(gdp_data)
#match country top gdp data
rates <- gdp_data$GDP[match(world_map$admin, gdp_data$Name)]
#create the map
#set the colours
qpal <- colorQuantile("Blues",
                      rates,
                      9)
#blank canvas
leaflet(world_map) %>% #what data to use
  
  #add tile
  addTiles() %>%
  
  #draw polygons on top of base map/ tile
  addPolygons(
    stroke = FALSE,
    smoothFactor = 0.2,
    fillOpacity = 1,
    color = ~qpal(rates)
    
  )

NA

Shiny and Leaflet

Using Leaflet with Shiny

library(shiny)
ui <- fluidPage(
  
  #create map canvas on page
  leafletOutput("mymap"),
  
  #creat a button and bind it to the recalc event
  actionButton("recalc", "New points")
  
)
server <- function(input, output, session) {
  
  #event handle, thios is in case for click event
  
  points<- eventReactive(input$recalc, {
    
    #calculate normal distribution random points around melbourne
    
    cbind(rnorm(40) * 3 + 145.0431, rnorm(40) -37.8773) 
    
  }, ignoreNULL = FALSE)
  
  
  output$mymap <- renderLeaflet({ #create leaflet map
    
    #blank canvas
    leaflet() %>%
      
      #add tile
      addTiles() %>%
      
      #use radomly generated points as markers on the map
      addMarkers(data = points())
    
  })
  
}
shinyApp(ui, server)

Listening on http://127.0.0.1:5271
NA
---
title: "Leaflet with R"
output: html_notebook
---

**Leaflet** is an open source JavaScript library for ineracticve maps:

- has built in basic interactivity like *zoom, pan* and *markers*
- can handle *shape files* using the ```rgdal``` package
- can integrate with Shiny app.



```{r}
#install the package
#install.packages("leaflet")

library(leaflet)
library(maps)


```


```{r}

m <- leaflet() %>%
  
  setView(lng = 145.0431, lat = -37.8773, zoom = 15) %>% 
  addTiles()
            
m
            
```
Note: that the use of ```%>%``` basically means **use the result of the previous function as the first parameter of the next function**.

The [magrittr](https://github.com/tidyverse/magrittr) package offers a set of operators which make your code more readable by:

  - structuring sequences of data operations left-to-right (as opposed to from the inside and out),
  - avoiding nested function calls,
  - minimizing the need for local variables and function definitions, and
  - making it easy to add steps anywhere in the sequence of operations.

Is the equiv of writing:

```{r}

#call the functionality of leaflet
m <- leaflet()

#set where the map is
m <- setView(m,
             lng = 145.0431, lat = -37.8773, zoom = 15) 

#Add graphics elements and layers to the map widget
m <- addTiles(m)
  

m

```

# Customise the tile 

[Leaflet providers](http://leaflet-extras.github.io/leaflet-providers/preview/index.html)

use help(addProviderTiles) to check other available tiles.

```{r}
m %>% addProviderTiles("Stamen.TonerLite")


```
#Placing Markers on Leaflet Maps

```{r}
#read some data
data <- read.csv("vet_schools_in_victoria.csv")
head(data)

```

```{r}
#dataset is very large so only place the first 30 record

leaflet(data = data[1:30, ] ) %>%
  
  #add the map
  addTiles() %>%
  
  #add some markers
  addMarkers(~longitude, #x values
             ~latitude, #y values
             
             #label that pops up when you click
             popup= ~as.character(location))

```

Now plot all the data:

```{r}

leaflet(data = data) %>%
  
  addTiles() %>%
  
  addMarkers(~longitude, #x values
             ~latitude, #y values
             
             #label that pops up when you click
             popup= ~as.character(location),
             
             #cluster markers when zooming out and in
             clusterOptions = markerClusterOptions())


```

#Plotting Choropleth Maps with Leaflet


```{r}
#preprocess that data

data <- read.csv("Household-heating-by-State-2008.csv", header=T) 

head(data)

#change the name of column 4 (X..Housing.Units.That.Are.Mobile.Homes) to Mobile homes
names(data)[4]<- "MobileHomes"

ag <- aggregate(MobileHomes~ States, #group by state
                FUN = mean, #find the mean number of mobile homes
                data = data) #in the data dataset

#make lower case as map data (see below) uses lower case!
ag$States <- tolower(ag$States)

head(ag)

```

```{r}

mapStates <- map("state", 
                  fill = TRUE, 
                  plot = FALSE)

#find the related rate for each state

rates <- ag$MobileHomes[match(mapStates$names, ag$States)]


#map the data

cpal <- colorNumeric("Blues", rates) # prepare the color mapping


#create the blak cavas
leaflet(mapStates) %>%
  
  addTiles() %>%
  
  addPolygons(
    stroke = FALSE,
    smoothFactor = 0.2,
    fillOpacity = 1,
    
    #use the rate of each state to find the correct colour
    color = ~cpal(rates)
  )

```

Not all the states appear...

```{r}
#print variables

mapStates$names

ag$States


```

We can see some different spelling:
- e.g. "virginia:chesapeake" ,  "washington:main", etc...

```{r}

# split the string with : as seperator
spliteNames <- strsplit(mapStates$names, ":") 


# get first part of the origin string;
# e.g. get washington from washington:san juan island
firstPartNames <- lapply(spliteNames, function(x) x[1])  
rates <- ag$MobileHomes[match(firstPartNames, ag$States)]

```

Now try again:

```{r}

# prepare the color mapping
cpal <- colorNumeric("Blues", rates) 

# create a blank canvas
leaflet(mapStates) %>% 
  
  # add tile
  addTiles() %>% 
  
  # draw polygons on top of the base map (tile)
  addPolygons( 
    stroke = FALSE, 
    smoothFactor = 0.2, 
    fillOpacity = 1,
    color = ~cpal(rates) # use the rate of each state to find the correct color
  )

```

#Shapefiles



```{r}
#install.packages(rdgal)
library(rgdal)

#read data
world_map <- readOGR("ne_50m_admin_0_countries.shp")
head(world_map)


gdp_data <- read.csv("WorldGDP.csv")

head(gdp_data)

```

```{r}
#match country top gdp data

rates <- gdp_data$GDP[match(world_map$admin, gdp_data$Name)]

#create the map

#set the colours
qpal <- colorQuantile("Blues",
                      rates,
                      9)

#blank canvas
leaflet(world_map) %>% #what data to use
  
  #add tile
  addTiles() %>%
  
  #draw polygons on top of base map/ tile
  addPolygons(
    stroke = FALSE,
    smoothFactor = 0.2,
    fillOpacity = 1,
    color = ~qpal(rates)
    
  )
  



```


#Shiny and Leaflet

[Using Leaflet with Shiny](https://rstudio.github.io/leaflet/shiny.html)

```{r}

library(shiny)


ui <- fluidPage(
  
  #create map canvas on page
  leafletOutput("mymap"),
  
  #creat a button and bind it to the recalc event
  actionButton("recalc", "New points")
  
)

server <- function(input, output, session) {
  
  #event handle, thios is in case for click event
  
  points<- eventReactive(input$recalc, {
    
    #calculate normal distribution random points around melbourne
    
    cbind(rnorm(40) * 3 + 145.0431, rnorm(40) -37.8773) 
    
  }, ignoreNULL = FALSE)
  
  
  output$mymap <- renderLeaflet({ #create leaflet map
    
    #blank canvas
    leaflet() %>%
      
      #add tile
      addTiles() %>%
      
      #use radomly generated points as markers on the map
      addMarkers(data = points())
    
  })
  
}

shinyApp(ui, server)

```

